C.3 Exploring reduced incidence of pediatric neuro-autoimmune disorders during COVID-19 restrictions
Bibliographic record
Abstract
Background: Infections are hypothesized to trigger certain autoimmune diseases; however, there is a lack of epidemiologic data surrounding pediatric neuro-autoimmune disorders during the COVID-19 pandemic. Our retrospective study assessed the incidence of pre-defined autoimmune disorders at the Children’s Hospital of Eastern Ontario from October 2017-June 2024. Methods: Inpatient/outpatient charts were queried to identify subjects with neuro-autoimmune disorders or type 1 diabetes (T1D) as a non-neurological autoimmune comparison group. Monthly incidences were compared between three COVID-19 pandemic restriction periods: the pre-restrictions (October 2017-March 2020), intra-restrictions (April 2020-June 2022), and post-restrictions periods (July 2022-June 2024). Poisson regression models were fit to the incidence data. To evaluate incidence of specific neuro-autoimmune disorders, crude monthly incidences of six diagnosis categories were compared: ‘Guillain-Barré syndrome’, ‘anti-NMDAR encephalitis’, ‘juvenile dermatomyositis’, ‘multiple sclerosis (MS)’, ‘acute demyelinating disorders’, and ‘other’. Results: Incidence of neuro-autoimmune disorders, but not T1D, decreased during the intra-restrictions period compared to the pre-restrictions period (IRR=0.57, 95% CI: 0.33-0.95, P<0.05). Grouping neuro-autoimmune subjects by diagnosis category showed a trend towards decreased incidence during the intra-restrictions versus pre-restrictions periods for all groups except MS. Conclusions: Incidence of certain neuro-autoimmune disorders, but not MS and T1D, decreased during pandemic restrictions, which may be due to reduced transmission of key infectious triggers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".